Statistical analysis of $k$-nearest neighbor collaborative recommendation

TL;DR

This paper introduces a probabilistic framework for collaborative recommendation systems, analyzing the asymptotic behavior of the cosine-type nearest neighbor method as user data increases.

Contribution

It proposes a general stochastic model for collaborative filtering and provides the first rigorous analysis of the statistical properties and convergence rates of a popular algorithm.

Findings

Proves consistency of the cosine-type nearest neighbor method under mild conditions.

Derives convergence rates for the recommendation accuracy as the number of users grows.

Provides illustrative examples demonstrating the theoretical results.

Abstract

Collaborative recommendation is an information-filtering technique that attempts to present information items that are likely of interest to an Internet user. Traditionally, collaborative systems deal with situations with two types of variables, users and items. In its most common form, the problem is framed as trying to estimate ratings for items that have not yet been consumed by a user. Despite wide-ranging literature, little is known about the statistical properties of recommendation systems. In fact, no clear probabilistic model even exists which would allow us to precisely describe the mathematical forces driving collaborative filtering. To provide an initial contribution to this, we propose to set out a general sequential stochastic model for collaborative recommendation. We offer an in-depth analysis of the so-called cosine-type nearest neighbor collaborative method, which is one of the most widely used algorithms in collaborative filtering, and analyze its asymptotic performance as the number of users grows. We establish consistency of the procedure under mild assumptions on the model. Rates of convergence and examples are also provided.